A method for detecting karst in a submarine tunnel

Abstract

This application relates to a method for karst detection in submarine tunnels, comprising: obtaining a first-level classification result of the karst development section of the area to be tested based on the obtained classification parameters of the area to be tested; obtaining classification parameters by employing several detection methods corresponding to the first-level classification result of the karst development section of the area to be tested, and obtaining a second-level classification result of the karst development section of the area to be tested based on the classification parameters; using the second-level classification result as the first-level classification result, and re-obtaining the second-level classification result, until the first-level classification result and the second-level classification result are the same. This method, through a closed-loop process of "preliminary classification—precise detection—dynamic adjustment—iterative verification—3D modeling," effectively controls engineering costs while ensuring detection accuracy, and is suitable for karst detection projects in marine environments with high water pressure and complex overburden layers.

Description

Technical Field

[0001] This application relates to the field of submarine tunnel construction technology, and in particular to a method for detecting karst in submarine tunnels. Background Technology

[0002] Karst geological hazards pose a significant risk to engineering construction, and the accuracy of their detection directly impacts construction safety and cost control. Compared to terrestrial environments, marine environments present unique challenges, including high water pressure, complex overburden layers (containing silt, sand, and other heterogeneous media), and limited construction sites. This makes many established terrestrial karst detection technologies difficult to directly adapt: ​​the high conductivity of seawater leads to rapid signal attenuation and reduced resolution in transient electromagnetic methods; the heterogeneity of seabed overburden interferes with the wave velocity inversion accuracy of elastic wave CT; and traditional fixed-spacing detection strategies easily create detection blind spots in areas with well-developed karst cavities, while resulting in wasted drilling costs in micro-developed areas. Achieving high-precision marine karst identification while ensuring detection efficiency has become a critical issue requiring breakthroughs in this field. Summary of the Invention

[0003] This application provides a method for detecting karst in submarine tunnels to address the problems of low efficiency and low accuracy in karst detection in marine areas in related technologies.

[0004] Firstly, a method for detecting karst formations in submarine tunnels is provided, comprising: Based on the obtained classification parameters of the area to be tested, the first classification result of the karst development section of the area to be tested is obtained; Based on the first-level classification result of the karst development section of the area to be tested, several detection methods corresponding to the first-level classification result are used to obtain the classification parameters, and the second-level classification result of the karst development section of the area to be tested is obtained based on the classification parameters. Use the second-level classification result as the first-level classification result, and re-obtain the second-level classification result until the first-level classification result and the second-level classification result are the same.

[0005] In some embodiments, the classification parameters include the area ratio of resistivity anomaly regions and the size of a single anomaly, wherein the area ratio of resistivity anomaly regions is the percentage of the area of ​​resistivity anomaly regions to the total area of ​​the geological longitudinal section, and the size of a single anomaly refers to the maximum linear size of a single resistivity anomaly on the longitudinal section. The area ratio of resistivity anomaly regions is preset with several percentage thresholds, and the size of a single anomaly is preset with several size thresholds. Based on the obtained classification parameters of the area to be tested, the first-level classification result of the karst development section of the area to be tested is obtained, including: The area ratio of resistivity anomalies and the size of individual anomalies in the measured region are compared with several ratio thresholds and several size thresholds. Based on the comparison results, the first-level classification of the karst development section of the area to be tested was obtained.

[0006] In some embodiments, the percentage thresholds include a first percentage threshold and a second percentage threshold, and the first percentage threshold is less than the second percentage threshold; Several size thresholds include a first size threshold and a second size threshold, wherein the first size threshold is smaller than the second size threshold; Based on the comparison results, the first-level classification of the karst development section of the area to be tested was obtained, including: If the area of ​​the resistivity anomaly region is less than the first proportion threshold and the size of a single anomaly is less than the first size threshold, then the current region to be tested is determined to be a first-level developmental stage. If the area of ​​the resistivity anomaly region is greater than the second proportion threshold, and the size of a single anomaly is greater than the second size threshold, then the current region to be tested is determined to be a third-level development stage. The rest are secondary developmental stages.

[0007] In some embodiments, based on the first-level classification result of the karst development section of the area to be tested, several detection methods corresponding to the first-level classification result are adopted, including: For the first-level development section, multi-frequency borehole sonar is used for detection, and when the size of the karst body detected exceeds the second size threshold, elastic wave CT test is added within the preset distance of the borehole. For the secondary developmental stage, a combination of elastography and borehole digital imaging was used for detection. For the third developmental stage, elastic wave CT and transap-hole radar are used for coordinated detection.

[0008] In some embodiments, for the third-level development section, when using elastic wave CT testing, the borehole spacing along the tunnel axis is set between a first distance and a second distance; For the secondary development section, when using elastic wave CT testing, the spacing of boreholes along the tunnel axis is controlled between the third and fourth distances. For the first-level development section, when using elastic wave CT testing, the spacing between boreholes along the tunnel axis should be controlled within a range not less than the fourth distance. Among them, the first distance < the second distance < the third distance < the fourth distance.

[0009] In some embodiments, the process of using the second-level classification result as the first-level classification result and re-acquiring the second-level classification result also includes dynamic upgrading and downgrading adjustments for the degree of karst development.

[0010] In some embodiments, the upgrade and adjustment steps specifically include: In the first-level development zone exploration, if the multi-frequency sonar data of multiple consecutive adjacent boreholes shows that the karst mass size is greater than or equal to the second size threshold, or if the single-hole linear dissolution rate of multiple consecutive adjacent boreholes is greater than the first linear dissolution rate threshold, the area is upgraded to the second-level development zone and re-explored according to the exploration method of the second-level development zone. In the detection of the secondary development section, if the area ratio of the abnormal zone in multiple consecutive elastic wave CT profiles is greater than the threshold of the second abnormal zone, or the linear dissolution rate of a single hole is greater than the threshold of the first linear dissolution rate, or a single karst body is detected and identified to be larger than the second size threshold and densely connected, the area is upgraded to the tertiary development section and re-detected according to the detection method of the tertiary development section.

[0011] In some embodiments, the downgrade adjustment step specifically includes: In the detection of the secondary development section, the area ratio of the abnormal area in multiple consecutive adjacent elastic wave CT profiles was less than the threshold of the first abnormal area, and no obvious karst morphology was observed in the borehole digital camera. After the karst body size was confirmed to be less than or equal to the first size threshold by multi-frequency sonar, the area was downgraded to the primary development section and further verified according to the detection method of the primary development section. In the detection of the third-level development section, the area ratio of the abnormal zone in multiple consecutive adjacent elastic wave CT profiles was ≤ the threshold of the second abnormal zone, and no large-scale connected karst cavities were observed. Therefore, the area was downgraded to the second-level development section, and the detection method of the second-level development section was used for supplementary verification.

[0012] In some embodiments, the method further includes: A three-dimensional geological BIM model is constructed based on the classification parameters of the area to be measured and the detection data obtained by several detection methods corresponding to the first-level classification results.

[0013] In some embodiments, a three-dimensional geological BIM model is constructed based on the classification parameters of the area to be measured and the detection data obtained by several detection methods corresponding to the first classification result. Specifically, this includes: By integrating the classification parameters of the area to be tested and the detection data obtained from several detection methods corresponding to the first-level classification results, a geological longitudinal profile map is generated. Using the tunnel axis as a reference, the modeling range is expanded by a preset multiple in both the vertical and horizontal directions, and a surface model is constructed based on marine topographic data; The surface model is integrated with the geological longitudinal profile map, and an interpolation algorithm is used to construct a three-dimensional geological initial model. Based on elastic wave CT profile data or cross-hole radar filling material identification results, the boundaries and properties of karst development sections in the initial three-dimensional geological model are corrected. The corrected 3D geological model is sectioned and assigned geological attributes, including the scale, shape, and type of infill material of the karst body, to complete the construction of the 3D geological BIM model.

[0014] This application provides a method for detecting karst in submarine tunnels. The method first utilizes transient electromagnetic methods for macroscopic surveys, enabling rapid screening and preliminary classification of karst development in large-area tunnels, thus improving detection efficiency. Then, based on the preliminary classification, a more refined detection method using differentiated matching is employed to verify the preliminarily classified karst development. This avoids the redundancy of traditional methods in low-development areas and the inadequacy of methods in high-development areas, achieving a match between detection resources and accuracy requirements, effectively balancing detection costs and effectiveness. By introducing a dynamic feedback adjustment mechanism, the karst development segment classification is upgraded or downgraded in real time based on the verification results, and re-verification is performed using the corresponding method based on the new classification. This forms a closed-loop detection process of "preliminary classification - precise verification - dynamic adjustment - iterative optimization," which can adaptively respond to the spatial variability of karst development, overcoming the shortcomings of fixed-zone detection that easily produces blind spots or waste, and improving the targeting and accuracy of submarine tunnel karst detection. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a method for detecting karst in a submarine tunnel provided in this application embodiment; Figure 2 This is a single-hole outline diagram of the multi-frequency borehole sonar method in the first-stage development section of the karst exploration method for submarine tunnels provided in the embodiments of this application; Figure 3 A cross-sectional view of the elastic wave CT test results of the third-level development section in the submarine tunnel karst detection method provided in this application embodiment (red represents bedrock, green represents karst). Figure 4 The three-dimensional geological model of the tunnel is provided in the submarine tunnel karst detection method in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] First, some of the technical terms used in this application will be explained to help those skilled in the art understand this application.

[0019] BIM: Building Information Modeling; CT: Computed Tomography; DEM: Digital Elevation Model; DK: Station number for fixed-distance mileage measurement.

[0020] This application provides a method for detecting karst in seabed tunnels, which can solve the problems of low efficiency and low accuracy in karst detection in marine areas in related technologies.

[0021] like Figure 1 As shown, a method for detecting karst formations in submarine tunnels includes: S100: Based on the obtained classification parameters of the area to be tested, obtain the first classification result of the karst development section of the area to be tested.

[0022] The core of this step is to first identify the karst-developed area to be tested, and then complete the preliminary classification of the karst development section through classification parameters. The specific implementation process is as follows: S101: Determine the area to be tested The area to be tested is a preliminarily identified karst development area within the tunnel site. Its determination process requires combining existing exploration data with geophysical exploration work. The specific steps are as follows: The first step is to analyze existing exploration data to fully understand the distribution range, elevation characteristics, and surrounding geological background of soluble rocks in the tunnel site area, providing a basis for subsequent geophysical exploration work.

[0023] The second step involves setting up geophysical survey lines and conducting surveys: Three survey lines are laid out longitudinally along the distribution range of soluble rock in the tunnel site area. All survey lines are parallel to the tunnel axis to ensure that the survey lines cover the entire soluble rock distribution area and achieve comprehensive detection of karst development. In the soluble rock area, the transient electromagnetic method in the water is used to carry out geophysical exploration. This method is suitable for the seabed environment and can effectively penetrate the overburden layer to obtain the resistivity information of the underground rock strata, thereby inverting the karst distribution characteristics.

[0024] The third step is the interpretation of geophysical data and the delineation of anomaly areas: professional interpretation of the detection data obtained by the transient electromagnetic method in the water area, focusing on the extraction of information on underground rock strata below the overburden layer, and preliminary investigation of clues to karst distribution; geophysical anomaly distribution characteristics refer to the characteristics of drastic changes in the detection data (especially resistivity) on a single survey line, such as the resistivity suddenly dropping from a high value to a low value, and then suddenly rising from a low value to a high value. Such a sudden change usually indicates a change in the properties of underground rock strata, and is likely a sign of moving from intact rock strata to fractured zones, karst cave areas, and then extending from karst cave areas back to intact rock strata.

[0025] The fourth step is to verify the anomaly section and determine the area to be measured: Select the points where geophysical anomalies change on each longitudinal survey line as change points. "Change points with the same nature" refers to change points on different survey lines that reflect the boundary of the same geological anomaly (for example, the low resistivity anomaly start points appear on all 3 survey lines, and these start points correspond to the leading edge boundary of the same karst area). Connect the change points with the same nature on the 3 survey lines in sequence to form a closed or semi-closed area, which is the geophysical anomaly section. Based on the above principles, in a specific engineering example, three resistivity anomaly areas were inverted in the sections DK11+455—DK11+525, DK11+530—DK11+650, and DK11+680—DK11+740. These anomaly areas exhibit low resistivity characteristics that extend laterally. Based on geological patterns, they are inferred to be karst development areas, and these sections are thus determined as the areas to be measured.

[0026] S102: Obtain grade classification parameters Through the above-mentioned transient electromagnetic method for water body detection and data interpretation, the classification parameters of the area to be measured are obtained. These parameters include the area ratio of resistivity anomaly regions and the size of individual anomalies, which are specifically defined as follows: Percentage of resistivity anomaly area: This refers to the percentage of the area of ​​the resistivity anomaly area delineated by interpretation when using the transient electromagnetic method in the water area to detect in the bedrock section, compared with the total area of ​​the geological longitudinal section of the detection section. It is used to reflect the overall density of karst development in the area to be tested.

[0027] Individual anomaly size: refers to the maximum linear size (which can be expressed as the maximum extension length or equivalent diameter) of a single independent resistivity anomaly delineated by the transient electromagnetic method in the water area on the longitudinal section of the tunnel geology. It is used to reflect the development scale of a single karst anomaly.

[0028] The percentage of resistivity anomaly area and the size of a single anomaly are used as two quantitative indicators for classification, making the classification results objective and repeatable, and facilitating the matching of subsequent detection methods.

[0029] S103: Determine the results of the first-level classification To achieve a preliminary classification of the degree of karst development, several proportional thresholds (corresponding to the area percentage of resistivity anomaly regions) and several size thresholds (corresponding to the size of a single anomaly body) are preset. The first-level classification of the karst development section in the area to be tested is completed by parameter comparison. The specific implementation is as follows: First, preset threshold standards: several ratio thresholds include a first ratio threshold and a second ratio threshold, and the first ratio threshold < the second ratio threshold; several size thresholds include a first size threshold and a second size threshold, and the first size threshold < the second size threshold.

[0030] Secondly, the grading judgment rules are as follows: The area ratio of resistivity anomalies and the size of individual anomalies in the area to be tested are compared with preset proportion thresholds and size thresholds, respectively. The karst development level is determined based on the comparison results, as follows: If the area ratio of the resistivity anomaly region is less than the first proportion threshold and the size of a single anomaly is less than the first size threshold, then the current test area is determined to be a primary developmental segment, i.e. a micro-developmental segment. If the area ratio of the resistivity anomaly region is greater than the second proportion threshold, and the size of a single anomaly is greater than the second size threshold, then the current test area is determined to be a third-level development segment, i.e., a strongly developed segment. The rest are secondary developmental stages.

[0031] In this embodiment, the preset threshold and corresponding grading standards are clearly defined as follows, which can be directly used in engineering practice: Primary developmental stage (micro-developmental stage): The area of ​​resistivity anomaly region accounts for <10%, and the size of a single anomaly is <1m; Level 3 development stage (strong development stage): The area of ​​resistivity anomaly region is >30%, and the size of a single anomaly is >5m. The rest are secondary developmental stages (intermediate developmental stages).

[0032] It should be noted that the preset threshold standard in this embodiment can be adjusted according to the engineering geological conditions and design requirements of different submarine tunnels to ensure that the grading results are consistent with the actual needs of the project. At the same time, through the grading in this step, the intensity of karst development in the area to be tested can be quickly determined, providing a grading basis for subsequent targeted detailed exploration and the formulation of prevention and control measures.

[0033] A large-scale, rapid survey was achieved using the transient electromagnetic method over water, ensuring detection efficiency while completing a preliminary classification of karst development levels. This avoided investing excessive resources in weakly developed areas and insufficient detection density in strongly developed areas. The preliminary classification results served as initial values ​​for iterative iterations, providing a benchmark for the closed-loop verification of S300.

[0034] S200: Based on the first-level classification result of the karst development section of the area to be tested, several detection methods corresponding to the first-level classification result are used to obtain the classification parameters, and the second-level classification result of the karst development section of the area to be tested is obtained based on the classification parameters.

[0035] Based on the first-level classification results obtained from S100, appropriate detection methods are matched for the first, second, and third-level developmental stages to achieve precise detection at each level. The specific detection procedures for each level are as follows: S201: Primary Developmental Stage Detection Procedure The primary development stage focuses on "precisely identifying minute karst features and verifying suspected anomalies," employing a detection method primarily based on multi-frequency borehole sonar supplemented by elastic wave CT for verification. The specific steps are as follows: S2011: Based on the karst influence range specified in the design documents, and combined with the test range and accuracy requirements of the multi-frequency borehole sonar method, geological borehole test points are reasonably arranged; at the same time, in order to ensure the effective identification of minute karst features such as fine cracks and small caves, the sonar frequency range is set to 50~200kHz.

[0036] S2012: After the geological borehole is completed, the casing inside the borehole is pulled out, provided that the borehole does not collapse, to provide a clear path for subsequent sonar testing.

[0037] S2013: After confirming that there is no interference from iron casing within the test range, conduct multi-frequency borehole sonar testing to comprehensively collect karst-related information of the surrounding rock strata.

[0038] S2014: After the test is completed, remove the remaining casing from the hole and seal the hole according to the specifications to prevent water from entering the hole and causing collapse, which would affect subsequent construction work.

[0039] S2015: Professionally interpret multi-frequency borehole sonar test data, generate relevant maps, and clarify the preliminary situation of karst development around the borehole.

[0040] During the exploration process, two key records are kept simultaneously: first, the core recovery rate within the borehole. When the core recovery rate is less than 80%, it indicates that the rock strata in the area may show signs of fracturing or karst development; second, the results of multi-frequency borehole sonar detection. When the detection finds that the size of the karst body exceeds the second size threshold (in this embodiment, the second size threshold is 5m), the corresponding sonar anomaly area is identified as a suspected karst development point. For both of these situations, supplementary elastic wave CT tests need to be conducted within a preset distance of the borehole (e.g., within 20 meters to meet the elastic wave CT test spacing requirements) to further verify the authenticity of the suspected karst development point and accurately identify hidden karst hazards.

[0041] The multi-frequency borehole sonar method exhibits high sensitivity to micro-sized karst formations, enabling precise detection of hidden, minute karst hazards. Elastic wave CT is only supplemented when the detected size exceeds a threshold, avoiding over-detection of primary developmental sections and effectively controlling costs.

[0042] S202: Secondary Developmental Stage Detection Procedure The secondary development zone exhibits moderate karst development. A combined detection mode of elastic wave CT and borehole digital imaging was employed, with dual-method fusion verification to improve the accuracy of karst detection. The specific steps are as follows: S2021: Based on the karst influence range provided in the design and combined with the test range requirements of elastic wave CT, geological borehole test points are arranged to ensure that the test covers the entire secondary development section.

[0043] S2022: After the geological drilling is completed, a 76mm diameter PVC pipe is lowered into the hole. The bottom end of the PVC pipe must extend to the bottom of the hole, and the connection between each section of the PVC pipe must be tight and seamless. After the PVC pipe is buried and fixed, the casing within the bedrock area is pulled out to avoid interfering with subsequent tests.

[0044] S2023: Simultaneously conduct cross-hole elastic wave CT test and borehole digital imaging test inside PVC pipe: The elastic wave CT adopts a single-hole excitation and multi-hole reception mode, with a sampling interval set at 0.5m; the borehole digital imaging adopts a panoramic scanning mode, generating a borehole wall unfolding image every 0.1m to fully capture the borehole wall dissolution characteristics.

[0045] S2024: After the test is completed, the elastic wave CT data and the borehole digital camera data are fused and interpreted. When the CT wave velocity anomaly area (wave velocity is 30% or more lower than normal rock mass) is consistent with the location of dissolution fissures and cavities shown in the borehole wall image, it is determined to be an effective karst development area.

[0046] S2025: After the interpretation is completed, pull out the PVC pipe and the remaining casing from the hole, and seal the hole according to the specifications.

[0047] S2026: Systematically interpret the verification results of collaborative exploration, generate relevant maps, and clarify the distribution range, scale, and morphology of karst in the secondary development stage.

[0048] Elastography provides information on the distribution of areas with abnormal wave velocity, while borehole digital imaging provides evidence of karst morphology on the borehole walls. The synergistic verification of these two technologies significantly improves the accuracy of karst identification and reduces the false positive rate.

[0049] S203: Detection Procedure for Tertiary Developmental Stages The karst development in the third-level development section is intense and poses a high risk of hidden dangers. A combined detection mode of elastic wave CT and cross-hole radar was adopted. Through intensive testing and the combination of the two methods, the type, scale, and infilling of the karst were accurately determined. The specific steps are as follows: S2031: Based on the karst influence range provided in the design, and combined with the technical requirements of joint testing of elastic wave CT and cross-hole radar, the geological borehole test points are densely arranged to increase the detection density and ensure full coverage of the strongly developed area.

[0050] S2032: After the geological drilling is completed, a 110mm diameter PVC pipe (with a reserved radar test channel) is lowered into the hole. The bottom end of the PVC pipe must extend to the bottom of the hole. The connection of each section of the PVC pipe must be tight and waterproof treatment must be done to prevent water seepage in the hole from interfering with the test data. After the PVC pipe is buried and fixed, the casing in the bedrock area is pulled out.

[0051] S2033: Simultaneously conduct cross-hole elastic wave CT test and cross-hole radar test inside PVC pipe: The elastic wave CT test parameters are the same as those in S202 (single-hole excitation, multi-hole reception, sampling interval 0.5m); the cross-hole radar adopts a one-transmit and one-receive mode, with a measurement point spacing of 0.2m, focusing on recording the intensity and waveform characteristics of the reflected signal to determine the type of karst filling.

[0052] S2034: After the test is completed, the two types of test data are fused and interpreted to determine the karst type: when the CT wave velocity anomaly area coincides with the radar strong reflection signal area, it is determined to be water-filled or silt-filled karst; when there is only CT wave velocity anomaly but no radar strong reflection signal, it is determined to be dry cave or karst filled with dense material.

[0053] S2035: After the interpretation is completed, pull out the PVC pipe and the remaining sleeve from the hole, and seal the hole according to the specifications to avoid safety hazards.

[0054] S2036: Systematically interpret the joint exploration results, generate relevant maps, clarify the distribution, scale, type and filling state of karst in the three-level development sections, and provide accurate basis for engineering prevention and control.

[0055] Elastic wave CT delineates the anomaly range, while cross-hole radar identifies the type of infill material (water-filled, silt, dry caves, etc.). The fusion of the two can accurately determine the scale, morphology, and infilling status of karst in highly developed areas.

[0056] For all three developmental stages mentioned above, elastic wave CT testing is employed. To achieve accurate and efficient detection of areas with different developmental levels and to reasonably control engineering costs, the spacing of boreholes along the tunnel axis is set according to the differences in developmental levels, satisfying the following conditions: first distance < second distance < third distance < fourth distance. In this embodiment, the first distance is 10m, the second distance is 12m, the third distance is 15m, and the fourth distance is 20m.

[0057] For the third-level development section, when using elastic wave CT testing, the borehole spacing along the tunnel axis is set between 10m and 12m. Considering the complex karst and significant hidden dangers in the highly developed section, reducing the borehole spacing decreases the detection blind zone and improves the detection resolution in complex and highly developed areas.

[0058] For secondary development sections, when using elastic wave CT testing, the borehole spacing along the tunnel axis is controlled between 15m and 20m. Considering the karst distribution characteristics of moderately developed sections, 15m is used for abnormally dense sections and 20m for sparse sections, balancing detection accuracy and cost-effectiveness.

[0059] For the first-level development section, when using elastic wave CT testing, the borehole spacing along the tunnel axis should be controlled to be no less than 20m. Considering the characteristics of karst micro-development and sparse distribution in the first-level development section, the maximum spacing is adopted. This maximizes detection efficiency and reasonably controls engineering costs while ensuring no obvious detection blind spots and meeting the verification requirements of micro-developed karst, thus avoiding resource waste caused by over-detection.

[0060] The above spacing settings form a clear hierarchical gradient: the spacing of the third-level development section is 10-12m (densified arrangement), the spacing of the second-level development section is 15-20m (adjusted as needed), and the spacing of the first-level development section is the largest and not less than 20m, which achieves the unity of detection accuracy and engineering economy.

[0061] Using the above methods, the classification parameters can be re-acquired, including the area ratio of resistivity anomaly regions and the size of individual anomalies. Quantitative and qualitative indicators such as the karst mass size, single-hole linear dissolution rate, area ratio of anomalies in elastic wave CT profiles, karst morphology characteristics of borehole walls, and cross-hole radar reflection signal characteristics can also be obtained for each borehole. This provides input for the dynamic adjustment of S300 and data support for the construction of the S400 BIM model. Specifically, the karst mass size refers to the actual size of the karst mass verified by drilling, representing the truly revealed anomalies; the single-hole linear dissolution rate refers to the ratio of the cumulative length of the karst section (including dissolution fissures and cavities) to the total length of the borehole during a single borehole drilling operation.

[0062] Due to the highly spatial variability and concealment of karst development, while the transient electromagnetic method used for preliminary classification can achieve rapid, large-scale surveys, its detection accuracy and resolution are limited, making it difficult to fully and accurately reflect the complex distribution of underground karst. If the detection plan is implemented rigidly based solely on the preliminary classification results, two situations may arise: First, in areas where the actual development level is higher than the preliminary level, insufficient accuracy of the detection method or excessively large borehole spacing may lead to the omission of large caves or interconnected karst bodies, creating detection blind spots and posing serious safety hazards to subsequent tunnel construction and operation. Second, in areas where the actual development level is lower than the preliminary level, the excessive use of high-precision, high-density detection methods will result in a large number of ineffective boreholes and redundant workload, significantly increasing project costs and time constraints.

[0063] Therefore, the process of using the second-level classification result as the first-level classification result and re-obtaining the second-level classification result also includes dynamic upgrading and downgrading adjustments of karst development degree.

[0064] Specifically, the upgrade mechanism operation process is as follows: In the verification of the S201 first-level development section, if the multi-frequency sonar detection data of two consecutive adjacent boreholes both show that the karst mass is ≥5m in size, the karst connectivity verification procedure will be initiated immediately. Supplementary observations of the rock core between the two boreholes were conducted using borehole digital imaging to calculate the linear dissolution rate; If the multi-frequency sonar data of two consecutive adjacent boreholes show that the karst mass size is greater than or equal to the second size threshold (5m in this embodiment), or if the single-hole linear dissolution rate of two consecutive adjacent boreholes is greater than the first linear dissolution rate threshold (30% in this embodiment), then the area is upgraded to a secondary development section, and boreholes are redeployed and exploration is carried out according to the detection method of the secondary development section (i.e., the coordinated detection of S202 elastic wave CT and borehole digital camera).

[0065] In the refined exploration of the S202 secondary development zone, if any of the following conditions are met, the karst development level of the area is directly determined to be upgraded to a tertiary development zone: ① The area ratio of the abnormal region in two or more consecutive elastic wave CT profiles is greater than the threshold of the second abnormal region (30% in this embodiment). ② The single-hole linear solubility rate is greater than the first linear solubility rate threshold (30% in this embodiment); ③ The detection identifies a single karst body with a size greater than the second size threshold (5m in this embodiment), and the karst body exhibits a dense and interconnected distribution.

[0066] After the upgrade, the drilling was densified and precise detection was carried out throughout the entire process, following the detection method of the three-stage development stage (i.e., the coordinated detection of elastic wave CT and cross-hole radar of S203).

[0067] The dynamic upgrade mechanism works by promptly upgrading the development level and simultaneously switching to a higher-precision detection method that matches the current level when actual karst development indicators discovered during refined exploration—such as the karst mass size revealed by multiple consecutive boreholes exceeding the second size threshold, the linear dissolution rate of a single borehole exceeding the first linear dissolution rate threshold, or the proportion of anomaly areas in elastic wave CT profiles consistently exceeding the second anomaly area threshold—exceed the current level's criteria. This mechanism avoids insufficient exploration due to inaccurate initial classification, ensuring that strongly developed areas are fully revealed by denser borehole drilling and higher-resolution detection methods, keeping hidden karst hazards within an identifiable and manageable range.

[0068] Specifically, the downgrade mechanism operation process is as follows: During the exploration of the S202 medium-development section, anomaly zones were statistically analyzed for three consecutive adjacent elastic wave CT profiles. If the area ratio of the anomaly zone in all three consecutive adjacent elastic wave CT profiles was less than the first anomaly zone threshold (5% in this embodiment), and no obvious karst morphology was observed in the borehole digital imaging, a downgrade assessment was initiated. After supplementary verification by multi-frequency sonar, once it was confirmed that the karst mass size was ≤ the first size threshold (1m in this embodiment), the area was downgraded to a first-level development section, and supplementary verification was performed using the exploration method for first-level development sections (i.e., the multi-frequency borehole sonar method of S201).

[0069] In the detection of the S203 strong development section, if the area ratio of the abnormal zone in three consecutive adjacent elastic wave CT profiles is ≤ the second abnormal zone threshold (30% in this embodiment), and no large-scale connected karst cavities are observed, then the area is downgraded to the second-level development section, and the detection method of the second-level development section (elastic wave CT and borehole digital camera collaborative detection) is used for supplementary verification.

[0070] If the grading parameters obtained again through steps S201 to S203 meet the grading conditions in step S100, namely, the area of ​​resistivity anomaly zone <10% and the size of a single anomaly <1m, it is a first-level development stage; the area of ​​resistivity anomaly zone >30% and the size of a single anomaly >5m, it is a third-level development stage; the rest are second-level development stages.

[0071] Using the above methods, if the actual development level is lower than expected, the level should be downgraded in a timely manner and the detection method simplified to avoid ineffective drilling and excessive detection, thereby reducing engineering costs.

[0072] The dynamic adjustment mechanism is the core component of the closed-loop iteration, used to correct the initial level of S100 based on the S200 detection results. The adjusted level directly triggers the switching of the corresponding detection method, and the updated level and parameters are input into S300 for a new round of verification, forming a closed loop of "detection-judgment-adjustment-re-detection".

[0073] S300: Use the second-level classification result as the first-level classification result, and re-acquire the second-level classification result until the first-level classification result and the second-level classification result are the same.

[0074] If the results of the first-level classification and the second-level classification are the same, it means that the preliminary classification of karst development level is consistent with the actual detection results. The original classification results are maintained, and the refined detection work in this area is completed. If the first-level classification result and the second-level classification result are different, the second-level classification result is taken as the first-level classification result. The system returns to the first-level classification result of the karst development section of the area to be tested, and uses several detection methods corresponding to the first-level classification result to obtain the classification parameters. Based on the classification parameters, the second-level classification result of the karst development section of the area to be tested is obtained.

[0075] For example, if the initial classification is a secondary development stage, but the actual detection parameters meet the criteria for a primary or tertiary development stage, the karst development level of the area will be upgraded or downgraded based on the actual detection results. If it is downgraded to a primary development stage, a detection method using multi-frequency borehole sonar as the main method and elastic wave CT as a supplementary verification will be adopted. If it is upgraded to a tertiary development stage, a detection mode using elastic wave CT and cross-hole radar in tandem will be adopted.

[0076] By repeatedly probing and verifying the second-level classification results as new first-level classification results until the two results are consistent, the final classification results are ensured to be highly consistent with the actual situation. The iterative mechanism can automatically compensate for possible deviations in the initial classification, so that the detection results converge to the actual karst development state and avoid the continuous propagation of scheme deviations caused by inaccurate classification in the first instance.

[0077] S400: Construct a three-dimensional geological BIM model based on the detection data obtained by several detection methods corresponding to the classification parameters of the area to be measured and the results of the first classification.

[0078] Specifically, it includes: S401: Integrate the detection data obtained from several detection methods corresponding to the classification parameters of the area to be tested and the results of the first classification to generate a geological longitudinal profile map.

[0079] The geological information obtained from steps S100 and S200 (including multi-source data such as multi-frequency sonar, elastic wave CT, borehole imaging, and cross-hole radar) was sorted out, and the geological longitudinal profile map was completed. In accordance with the BIM modeling requirements, control geological profiles were added in areas with strong karst development. Transient electromagnetic survey data and refined verification data at various levels were integrated to achieve the fusion and utilization of multi-source heterogeneous data.

[0080] S402: Using the tunnel axis as a reference, expand the modeling range by a preset multiple in both the vertical and horizontal directions, and construct a surface model based on marine topographic data.

[0081] The scope of the project was clearly defined. To ensure the accuracy of the calculations, the actual calculation area was expanded by a factor of 2 in both the vertical and horizontal directions. A surface model of the project area was constructed using marine topographic maps or DEM data. The surface model was then integrated with geological longitudinal sections and geological profiles to achieve the conversion to three-dimensional space.

[0082] S403: Integrate the surface model with the geological longitudinal profile map, and use an interpolation algorithm to construct a three-dimensional geological initial model; S404: Based on elastic wave CT profile data or cross-hole radar filling material identification results, the boundaries and properties of karst development sections in the initial three-dimensional geological model are corrected.

[0083] In areas with strong karst development, the three-dimensional model is targeted for intervention and correction based on the results of elastic wave CT profile and cross-hole radar infill identification to ensure that the boundary positioning error of the karst body is ≤0.5m.

[0084] S405: The modified three-dimensional geological model is sectioned and assigned geological attributes, including the scale, shape, and type of infill material of the karst body, to complete the construction of the three-dimensional geological BIM model, providing intuitive and quantifiable three-dimensional geological basis for tunnel design, construction and risk prevention.

[0085] Through the organic combination of the above steps, this method forms a complete technical chain of "macro-survey - preliminary classification - precise detection - dynamic adjustment - iterative verification - 3D modeling". While ensuring detection accuracy, it achieves optimized allocation of detection resources and effectively solves the problem of balancing efficiency and accuracy in marine karst detection.

[0086] In summary, the beneficial effects of this invention are as follows: 1. An innovative differentiated technical system of "karst development level - detection method - hole spacing parameter" was constructed. Multi-frequency borehole sonar and gradient hole spacing elastic wave CT and other methods were matched for different development stages of micro, medium and strong karst, which solved the technical bottleneck of accurate detection of marine karst and improved the detection efficiency by more than 30% compared with traditional methods. 2. It pioneered a multi-source data fusion-based 3D geological BIM modeling process. Through transient electromagnetic macroscopic division, refined detection data constraints, and CT profile correction, it achieved a technical closed loop of "planar partitioning - profile refinement - 3D modeling". The model's spatial positioning error of karst bodies is controlled within 0.5m, providing visualized decision support for risk warning and dynamic design of submarine tunnel construction. 3. Overcoming the limitations of land-based exploration technology in adaptability to marine environments, by combining transient electromagnetic methods with drilling and geophysical exploration technologies, the interference of high water pressure and complex overburden layers on the detection signals is overcome, achieving seamless integration from macro-level regional division to local fine-scale exploration, and significantly reducing the misjudgment rate of marine karst disasters.

[0087] It should be noted that the technical solution of this invention is not a simple superposition of existing detection methods, but a systematic and innovative integration based on the special marine environment. In existing technologies, while single methods such as multi-frequency borehole sonar, elastic wave CT, and cross-hole radar have been applied to karst detection, none have formed an adaptation logic of "development level-method-parameters," nor are they supported by a dynamic adjustment mechanism. For example, the related technology of Dalian Metro Line 5 only achieves fixed-spacing detection in two zones, which cannot respond to the spatial variability of karst; the patent for joint land-based detection does not optimize parameters for high water pressure and thick overburden in the sea, resulting in significant signal interference problems. This invention achieves macroscopically precise zoning through optimization of transient electromagnetic parameters in the water area, then matches specific detection schemes and gradient borehole spacing for micro, medium, and strong development sections, embeds a dynamic upgrade / downgrade mechanism to achieve real-time iteration of the detection strategy, and finally realizes the results through multi-source data fusion BIM modeling, forming a closed-loop technical system of "zoning-detection-optimization-application." This not only overcomes the adaptation defects of single methods in marine environments, but also achieves a balance between detection accuracy, efficiency, and economy through the synergistic effect of various innovations.

[0088] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0089] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for detecting karst formations in submarine tunnels, characterized in that, It includes: Based on the obtained classification parameters of the area to be tested, the first classification result of the karst development section of the area to be tested is obtained; Based on the first-level classification result of the karst development section of the area to be tested, several detection methods corresponding to the first-level classification result are used to obtain the classification parameters, and the second-level classification result of the karst development section of the area to be tested is obtained based on the classification parameters. Use the second-level classification result as the first-level classification result, and re-obtain the second-level classification result until the first-level classification result and the second-level classification result are the same.

2. The method for detecting karst in submarine tunnels as described in claim 1, characterized in that, The classification parameters include the area ratio of resistivity anomaly regions and the size of a single anomaly. The area ratio of resistivity anomaly regions is the percentage of the area of ​​resistivity anomaly regions to the total area of ​​the geological longitudinal section, and the size of a single anomaly refers to the maximum linear size of a single resistivity anomaly on the longitudinal section. The area ratio of resistivity anomaly regions is preset with several percentage thresholds, and the size of a single anomaly is preset with several size thresholds. Based on the obtained classification parameters of the area to be tested, the first-level classification result of the karst development section of the area to be tested is obtained, including: The area ratio of resistivity anomalies and the size of individual anomalies in the measured region are compared with several ratio thresholds and several size thresholds. Based on the comparison results, the first-level classification of the karst development section of the area to be tested was obtained.

3. The method for detecting karst in submarine tunnels as described in claim 2, characterized in that, The percentage thresholds include a first percentage threshold and a second percentage threshold, and the first percentage threshold is less than the second percentage threshold; Several size thresholds include a first size threshold and a second size threshold, wherein the first size threshold is smaller than the second size threshold; Based on the comparison results, the first-level classification of the karst development section of the area to be tested was obtained, including: If the area of ​​the resistivity anomaly region is less than the first proportion threshold and the size of a single anomaly is less than the first size threshold, then the current region to be tested is determined to be a first-level developmental stage. If the area of ​​the resistivity anomaly region is greater than the second proportion threshold, and the size of a single anomaly is greater than the second size threshold, then the current region to be tested is determined to be a third-level development stage. The rest are secondary developmental stages.

4. The method for detecting karst in submarine tunnels as described in claim 3, characterized in that, Based on the first-level classification results of the karst development sections of the area to be tested, several detection methods corresponding to the first-level classification results are adopted, including: For the first-level development section, multi-frequency borehole sonar is used for detection, and when the size of the karst body detected exceeds the second size threshold, elastic wave CT test is added within the preset distance of the borehole. For the secondary developmental stage, a combination of elastography and borehole digital imaging was used for detection. For the third developmental stage, elastic wave CT and transap-hole radar are used for coordinated detection.

5. The method for detecting karst in submarine tunnels as described in claim 4, characterized in that, For the third-level development section, when using elastic wave CT testing, the borehole spacing along the tunnel axis is set between the first distance and the second distance. For the secondary development section, when using elastic wave CT testing, the spacing of boreholes along the tunnel axis is controlled between the third and fourth distances. For the first-level development section, when using elastic wave CT testing, the spacing between boreholes along the tunnel axis should be controlled within a range not less than the fourth distance. Among them, the first distance < the second distance < the third distance < the fourth distance.

6. The method for detecting karst in submarine tunnels as described in claim 4, characterized in that, The process of using the second-level classification result as the first-level classification result and then re-obtaining the second-level classification result also includes dynamic upgrading and downgrading adjustments of karst development degree.

7. The method for detecting karst in submarine tunnels as described in claim 6, characterized in that, The specific upgrade and adjustment steps are as follows: In the first-level development zone exploration, if the multi-frequency sonar data of multiple consecutive adjacent boreholes shows that the karst mass size is greater than or equal to the second size threshold, or if the single-hole linear dissolution rate of multiple consecutive adjacent boreholes is greater than the first linear dissolution rate threshold, the area is upgraded to the second-level development zone and re-explored according to the exploration method of the second-level development zone. In the detection of the secondary development section, if the area ratio of the abnormal zone in multiple consecutive elastic wave CT profiles is greater than the threshold of the second abnormal zone, or the linear dissolution rate of a single hole is greater than the threshold of the first linear dissolution rate, or a single karst body is detected and identified to be larger than the second size threshold and densely connected, the area is upgraded to the tertiary development section and re-detected according to the detection method of the tertiary development section.

8. The method for detecting karst in submarine tunnels as described in claim 6, characterized in that, The specific steps for downgrading are as follows: In the detection of the secondary development section, the area ratio of the abnormal area in multiple consecutive adjacent elastic wave CT profiles was less than the threshold of the first abnormal area, and no obvious karst morphology was observed in the borehole digital camera. After the karst body size was confirmed to be less than or equal to the first size threshold by multi-frequency sonar, the area was downgraded to the primary development section and further verified according to the detection method of the primary development section. In the detection of the third-level development section, the area ratio of the abnormal zone in multiple consecutive adjacent elastic wave CT profiles was ≤ the threshold of the second abnormal zone, and no large-scale connected karst cavities were observed. Therefore, the area was downgraded to the second-level development section, and the detection method of the second-level development section was used for supplementary verification.

9. The method for detecting karst in submarine tunnels as described in claim 4, characterized in that: The method further includes: A three-dimensional geological BIM model is constructed based on the classification parameters of the area to be measured and the detection data obtained by several detection methods corresponding to the first-level classification results.

10. The method for detecting karst in submarine tunnels as described in claim 9, characterized in that, Based on the classification parameters of the area to be measured and the detection data obtained by several detection methods corresponding to the first-level classification results, a three-dimensional geological BIM model is constructed, specifically including: By integrating the classification parameters of the area to be tested and the detection data obtained from several detection methods corresponding to the first-level classification results, a geological longitudinal profile map is generated. Using the tunnel axis as a reference, the modeling range is expanded by a preset multiple in both the vertical and horizontal directions, and a surface model is constructed based on marine topographic data; The surface model is integrated with the geological longitudinal profile map, and an interpolation algorithm is used to construct a three-dimensional geological initial model. Based on elastic wave CT profile data or cross-hole radar filling material identification results, the boundaries and properties of karst development sections in the initial three-dimensional geological model are corrected. The corrected 3D geological model is sectioned and assigned geological attributes, including the scale, shape, and type of infill material of the karst body, to complete the construction of the 3D geological BIM model.

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